Hybrid-learning-based classification and quantitative inference of driver braking intensity of an electrified vehicle

The recognition of driver's braking intensity is of great importance for advanced control and energy management for electric vehicles. In this paper, the braking intensity is classified into three levels based on novel hybrid unsupervised and supervised learning methods. First, instead of selecting threshold for each braking intensity level manually, an unsupervised Gaussian Mixture Model is used to cluster the braking events automatically with brake pressure. Then, a supervised Random Forest model is trained to classify the correct braking intensity levels with the state signals of vehicle and powertrain. To obtain a more efficient classifier, critical features are analyzed and selected. Moreover, beyond the acquisition of discrete braking intensity level, a novel continuous observation method is proposed based on Artificial Neural Networks to quantitative analyze and recognize the brake intensity using the prior determined features of vehicle states. Experimental data are collected in an electric vehicle under real-world driving scenarios. Finally, the classification and regression results of the proposed methods are evaluated and discussed. The results demonstrate the feasibility and accuracy of the proposed hybrid learning methods for braking intensity classification and quantitative recognition with various deceleration scenarios

Yelp Catering Reviews Usefulness Prediction

With the widespread of online businesses, evaluation of customers' feedback is important for the online recommender systems because online reviews have become one of the most important sources of information for modern consumers before purchasing goods or using services. Many recommender systems use user-generated 'usefulness votes' in order to prioritize reviews for users, but there is much room for improvement. In this work, we attempt to predict the the usefulness vote a user will give to the reviews listed in the restaurant category. Using all features, a binary stacked ensemble model achieved a high level of accuracy (0.83). Several feature groups yielded statistically significant improvements while the features related with content don't have great impact to the usefulness. The authors present the results of the study and discuss their significance for research and practice.Master of Science in Information Scienc

A Framework for Prognostics Reasoning

The use of system data to make predictions about the future system state commonly known as prognostics is a rapidly developing field. Prognostics seeks to build on current diagnostic equipment capabilities for its predictive capability. Many military systems including the Joint Strike Fighter (JSF) are planning to include on-board prognostics systems to enhance system supportability and affordability. Current research efforts supporting these developments tend to focus on developing a prognostic tool for one specific system component. This dissertation research presents a comprehensive literature review of these developing research efforts. It also develops presents a mathematical model for the optimum allocation of prognostics sensors and their associated classifiers on a given system and all of its components. The model assumptions about system criticality are consistent with current industrial philosophies. This research also develops methodologies for combine sensor classifiers to allow for the selection of the best sensor ensemble

Machine learning on Web documents

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (leaves 111-115).The Web is a tremendous source of information: so tremendous that it becomes difficult for human beings to select meaningful information without support. We discuss tools that help people deal with web information, by, for example, blocking advertisements, recommending interesting news, and automatically sorting and compiling documents. We adapt and create machine learning algorithms for use with the Web's distinctive structures: large-scale, noisy, varied data with potentially rich, human-oriented features. We adapt two standard classification algorithms, the slow but powerful support vector machine and the fast but inaccurate Naive Bayes, to make them more effective for the Web. The support vector machine, which cannot currently handle the large amount of Web data potentially available, is sped up by "bundling" the classifier inputs to reduce the input size. The Naive Bayes classifier is improved through a series of three techniques aimed at fixing some of the severe, inaccurate assumptions Naive Bayes makes. Classification can also be improved by exploiting the Web's rich, human-oriented structure, including the visual layout of links on a page and the URL of a document. These "tree-shaped features" are placed in a Bayesian mutation model and learning is accomplished with a fast, online learning algorithm for the model. These new methods are applied to a personalized news recommendation tool, "the Daily You." The results of a 176 person user-study of news preferences indicate that the new Web-centric techniques out-perform classifiers that use traditional text algorithms and features. We also show that our methods produce an automated ad-blocker that performs as well as a hand-coded commercial ad-blocker.by Lawrence Kai Shih.Ph.D

Variable precision rough set theory decision support system: With an application to bank rating prediction

This dissertation considers, the Variable Precision Rough Sets (VPRS) model, and its development within a comprehensive software package (decision support system), incorporating methods of re sampling and classifier aggregation. The concept of /-reduct aggregation is introduced, as a novel approach to classifier aggregation within the VPRS framework. The software is applied to the credit rating prediction problem, in particularly, a full exposition of the prediction and classification of Fitch's Individual Bank Strength Ratings (FIBRs), to a number of banks from around the world is presented. The ethos of the developed software was to rely heavily on a simple 'point and click' interface, designed to make a VPRS analysis accessible to an analyst, who is not necessarily an expert in the field of VPRS or decision rule based systems. The development of the software has also benefited from consultations with managers from one of Europe's leading hedge funds, who gave valuable insight, advice and recommendations on what they considered as pertinent issues with regards to data mining, and what they would like to see from a modern data mining system. The elements within the developed software reflect each stage of the knowledge discovery process, namely, pre-processing, feature selection, data mining, interpretation and evaluation. The developed software encompasses three software packages, a pre-processing package incorporating some of the latest pre-processing and feature selection methods a VPRS data mining package, based on a novel "vein graph" interface, which presents the analyst with selectable /-reducts over the domain of / and a third more advanced VPRS data mining package, which essentially automates the vein graph interface for incorporation into a re-sampling environment, and also implements the introduced aggregated /-reduct, developed to optimise and stabilise the predictive accuracy of a set of decision rules induced from the aggregated /-reduct

Clasificación mediante conjuntos

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid. Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura: 15-02-200